CN106203212B - A kind of binary tree RFID anti-collision method based on dynamic frame slot - Google Patents
A kind of binary tree RFID anti-collision method based on dynamic frame slot Download PDFInfo
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- G06K7/10019—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves resolving collision on the communication channels between simultaneously or concurrently interrogated record carriers.
- G06K7/10029—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves resolving collision on the communication channels between simultaneously or concurrently interrogated record carriers. the collision being resolved in the time domain, e.g. using binary tree search or RFID responses allocated to a random time slot
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- G06K7/10049—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation sensing by radiation using wavelengths larger than 0.1 mm, e.g. radio-waves or microwaves resolving collision on the communication channels between simultaneously or concurrently interrogated record carriers. the collision being resolved in the time domain, e.g. using binary tree search or RFID responses allocated to a random time slot interrogator driven, i.e. synchronous binary tree
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Abstract
A kind of binary tree RFID anti-collision method based on dynamic frame slot, the present invention has merged the thought of the existing label anti-collision algorithm of two classes, total number of labels to be identified is pre-estimated using Vogt algorithm, dynamic Frame Slotted Aloha algorithm is chosen to identify label, unidentified tag extraction is come out to the judgement for carrying out collision bit, combines binary tree algorithm to carry out collision labels division according to the case where collision bit.Simulation result shows: DFBT algorithm shortens recognition time, reduces required total timeslot number and improve recognition efficiency, when number of tags reaches 1000 or so, system identification efficiency still may remain in 64% or so, 210% and 30% has been respectively increased than dynamic Frame Slotted Aloha algorithm and retrusive binary search tree algorithm, the method of the present invention has high stability and low cost simultaneously, has a good application prospect in current internet of things field.
Description
Technical field
The invention belongs to the multi-tags in radio RF recognition technology field to read technology, be related to solving in RFID system
The method of multi-tag collision problem.
Background technique
Radio frequency identification (Radio Frequency Identification, the RFID) core of technology as Internet of Things
One of technology, just as the fast development of Internet of Things is widely used every field.Radio RF recognition technology is a kind of
Using spatial electromagnetic wave as the non-contact data automatic acquisition technology of transmission medium, the basic principle is that using electromagnetic propagation and penetrating
Frequency signal realizes the automatic identification to object to be identified.Compared with traditional identification technology, it can it is non-contact, non-optical can
Depending on completing information input and processing in the case of, no manual intervention, have that easy to operate, amount of storage is big, good confidentiality, reaction time
The advantages that short, strong to environmental suitability, the fields such as gate inhibition, traffic, food safety and logistics have been widely used in it.Typically
RFID system is generally made of electronic tag (Tag), reader (Reader) and back-end data base (Database) three parts,
In the radio-frequency recognition system of multiple readers and multiple labels, there is reader collision and tag-collisions, due to reader
The probability to collide is relatively small, and reader itself has stronger information processing capability, so current research relatively collects
In in tag-collision, i.e., when there are two or more than two labels simultaneously to reader send data when, reader just will appear
Data collision is received, causes reader can not correct identification tag information.
In order to solve multi-tag collision problem, many label anti-collision algorithms have been proposed at present, mainly there are two major classes:
Nondeterministic algorithm based on ALOHA algorithm and the deterministic algorithm based on binary tree.ALOHA algorithm is rung at random by label
Tag state should be detected with reader to realize information exchange, mainly include pure ALOHA (PureALOHA, PA) algorithm, time slot
ALOHA (Slotted ALOHA, SA) algorithm, Frame Slotted Aloha (Frame-slotted ALOHA, FSA) algorithm and dynamic frame
CDMA slotted ALOHA (Dynamic Frame-slotted ALOHA, DFSA) algorithm etc..The advantages of these algorithms is that design is simple, mark
Label and reader all do not need to execute complicated operation, but when number of tags increases, the Probability maximum that label collides is calculated
Method reduced performance, and due to Slot selection there are it is certain a possibility that, certain labels it is possible that for a long time be not identified.
Binary tree search algorithm is chiefly used in hyper band, be namely based on bit manipulation two that the Class0 standard of EPC Global uses
Binary search tree algorithm successively identifies unique tag ID, existing binary tree search algorithm by successive appraximation
(Dynamic Binary Search Tree, DBST), retrusive Binary tree search algorithm (Regressive-style
Binary Search Tree, RBST), great-jump-forward dynamic tree algorithm (Jumping and Dynamic Searching Tree,
JDST) and inquire tree algorithm (Query Tree, QT) etc..Although such algorithm is able to achieve 100% label read rate, when mark
When label digit is larger, i.e., when number of tags is huge, the frequency of collision can be greatly increased, and collision can only generate two points every time
Branch, recognition time can sharply increase, and search efficiency can reduce.
Summary of the invention
The purpose of the present invention is overcome the deficiencies in the prior art, propose a kind of binary tree RFID based on dynamic frame slot
Collision-proof method.The present invention has merged ALOHA algorithm and Binary tree search algorithm proposes the binary tree of dynamic frame slot
(Dynamic Framed Binary Tree, DFBT) anti-collision algorithm, experiment simulation show that the present invention is marked than existing RFID
Sign anti-collision algorithm throughput with higher and recognition efficiency.
The present invention is achieved by the following technical solutions.
1, Vogt algorithm estimation label number.
Number of labels estimation plays a crucial role the performance of algorithm, the first estimation label quantity before identifying label
It is for more preferable more accurately dynamic adjustment frame length, so that algorithm performance reaches maximum.Existing number of labels estimation is calculated at present
Method mainly has: Vogt algorithm, Schoute algorithm and Low Bound algorithm etc..When number of tags is huge, Vogt algorithm is estimated
Meter number of labels can efficiently reduce error.
It is L for frame length, number of tags to be identified is N, available using Chebyshev inequality:
<c0,c1,ck> it is previous reading cycle as a result, being to measure free timeslot, identification time slot and collision time slot respectively
Free timeslot number, success timeslot number and the collision timeslot number obtained after change.<a0,a1,ak> it is free timeslot, success time slot respectively
With the desired value of collision time slot.
Some label occupies the probability of any time slot in frame are as follows:
Same time slot has the probability of M label are as follows:
Probability without request identification label in one time slot are as follows:
The probability of only one request label in one time slot are as follows:
There are two in one time slot or more than two requests identify the probability of label are as follows:
Pc=1-Ps-Pi (6)
Then after the recognition cycle of a frame, without the desired value a of label timeslot number0, only label timeslot number
Desired value a1, generate the desired value a of collision timeslot numberkIt is respectively as follows:
ak=L × Pc=L-a0-a1 (9)
2, the method for the present invention describes.
The optimization analysis of 2.1 frame lengths.
By the rule of Frame Slotted Aloha algorithm distribution time slot it is found that frame length L is necessary for 2 integer power, i.e. L=2m, this hair
What m was represented in bright method is the digit for determining time slot, and such label can determine to select in a frame according to the position m in self ID
The timeslot number selected.
The identical probability in a certain position of certain n label are as follows:
The probability for having n label in one time slot while sending are as follows:
When total number of labels is N, there is the probability of n label while transmission in some time slot are as follows:
A certain time slot is the probability of successfully time slot are as follows:
The desired value S of success number of time slots in one frame1Are as follows:
To S1Derivation, enabling its derived function is 0, and the minimum value for obtaining desired value is m value:
That is:
Then the method for the present invention frame length are as follows:
WhereinIndicate downward rounding operation, so that the relational expression of frame length L Yu number of tags N to be identified have been obtained, in order to
Illustrate that the method for the present invention frame length is optimized, will be compared with DFSA algorithm, as shown in table 1:
The comparison of table 1DFSA algorithm and inventive algorithm frame length
| Number of tags | 50 | 100 | 200 | 300 | 400 | 500 | 600 | 700 | 800 | 900 | 1000 |
| DFSA frame length | 50 | 100 | 200 | 300 | 400 | 500 | 600 | 700 | 800 | 900 | 1000 |
| Frame length of the present invention | 32 | 64 | 128 | 256 | 256 | 256 | 512 | 512 | 512 | 512 | 512 |
2.2 distribution time slot rules.
In order to reduce label impact several times, label selection time slot rule dictates: reader sends querying command first,
Label to be identified checks oneself ID after receiving order, and satisfactory label response, then reader detects responsive tags most
High collision bit k, then responsive tags selection timeslot number collides the position m that k are risen by the highest of ID and determines, label distributes time slot schematic diagram
As shown in Figure 1.Such as having 4 labels in hypothesis system, ID is respectively 1000,1101,0100 and 0011, is obtained according to formula (16)
M=2 out, reader detect highest collision bit k=3, then label selection timeslot number is determined by the 3rd and the 2nd in ID code, i.e.,
(10)2=2, (11)2=3, (01)2=1, (00)2=0, therefore they are responded respectively in time slot 2, time slot 3, time slot 1 and time slot 0
In.
2.3 specific execution steps.
In general, in RFID system, containing there are two counter LSC and RSC, LSC to be defined as touching in each label
Hitting position is " 0 " label counter, and it is " 1 " label counter that RSC, which is defined as collision bit, and Fig. 2 is that the present invention proposes DFBT algorithm stream
Cheng Tu, steps are as follows for specific execution:
(S01): before carrying out reading data, estimating the number N of label to be identified with Vogt algorithm first.
(S02): calculating separately out the value of the value and frame length L that determine label selection timeslot number digit m.
(S03): time slot scanning is carried out, reader sends Query (m, L) and orders to each label, after label receives order,
Timeslot number i needed for being selected in (0, L-1) time slot respectively according to distribution time slot rule.
(S04): reader judges the state of i-th of time slot.Then execute following three kinds of situations one of:
If 1) time slot is that successfully time slot, total number of labels N=N-1 (number N of label to be identified subtracts 1), the wheel identify
After go to step (S09)；
If 2) time slot is empty slot, go to step (S01), carries out next round identification.
If 3) time slot is collision time slot, all collision labels go to step (S05).
(S05): the highest collision bit k value of the collision labels ID code of reader detection on last stage, and detect and record
The size of numerical value q on highest collision bit k.
(S06): tag ID highest collision bit and prefix are sent to each collision labels by reader, with collision labels ID sheet
Body comparison, if detecting the label highest collision bit of transmission and prefix is identical with collision labels ID and highest collision bit is " 0 ",
Then counter LSC is from adding 1, if collision bit is " 1 ", counter RSC adds 1 certainly.
(S07): reader is entered a judgement: the label of all q=1 responds the right subtree in binary tree, all q=0's
Label responds the left subtree in binary tree, obtains the value of counter LSC and counter RSC, i.e., is responded respectively in binary tree
Left subtree and right subtree on number of tags.
(S08): the value of counter LSC and counter RSC are judged:
If LSC > 2 and RSC > 2, step (S02) is jumped to；
If LSC=2 and RSC=2, step (S05) is jumped to；
If LSC=1 and RSC=1, show this wheel end of identification, then the total number of labels N=N-2 (number of label to be identified
N subtracts 2).
(S09): whether label is all identified after judging last round of end of identification: if total number of labels (label to be identified
Number) N > 1, care label does not identify all, then jumps to step (S01), carry out the identification of next round, until whole labels are known
It does not complete；If when total number of labels (number of label to be identified) N=0, care label all complete by identification, then identification knot is searched for
Beam.
The present invention, which leads to the problem of multi-tag collision in order to improve reader during identifying multiple labels, to be existed, in conjunction with
Existing uncertain and two class multi-label anti-collision algorithm of certainty, has merged ALOHA algorithm and Binary tree search algorithm,
Propose a kind of anticollision side binary tree (Dynamic Framed Binary Tree, DFBT) based on dynamic frame slot
Method.Number of tags to be identified is pre-estimated using Vogt algorithm, dynamic adjusts frame length, realizes in conjunction with Binary tree search algorithm
Identification to label.Its simulation analysis also shortens label the result shows that this method not only increases the recognition efficiency of system
Recognition time, when number of tags reaches 1000 or so, system identification efficiency still may remain in 64% or so, than dynamic frame slot
210% and 30% has been respectively increased in ALOHA algorithm and retrusive binary search tree algorithm.Pass through the theoretical performance to this method
Analysis obtains, the theoretical value of recognition efficiency and the error of experiment value are less than 2%, within the scope of error allows, theoretical value with
Experiment value is consistent substantially, while this method can improve system stability, has good application in current internet of things field
Prospect.
Binary system RFID anti-collision method of the present invention based on dynamic frame slot, it is characterized in that combining dynamic frame
CDMA slotted ALOHA anti-collision algorithm and binary search tree anti-collision algorithm, and frame length is optimized and label selection timeslot number
Rule is adjusted, during identifying whole labels, total timeslot number and recognition time required for can not only having reduced, but also can
To improve system identification efficiency and increase system stability.
Detailed description of the invention
Fig. 1 is that the present invention proposes that DFBT algorithm distributes time slot rule schematic diagram.
Fig. 2 is that the present invention proposes DFBT algorithm flow chart.
Fig. 3 is that the present invention proposes DFBT algorithm numerical example schematic diagram.
Fig. 4 is that the total timeslot number theory analysis of the present invention and simulation analysis compare.
Fig. 5 is that recognition efficiency theory analysis of the present invention and simulation analysis compare.
Fig. 6 is that the present invention is compared with the total timeslot number of other algorithms.
Fig. 7 is that the present invention is compared with other algorithm recognition efficiencies.
Fig. 8 is the present invention and the collision timeslot number comparison of other algorithms.
Fig. 9 is that the present invention is compared with other algorithm recognition times.
Figure 10 is the recognition efficiency comparison that the present invention proposes DFBT algorithm difference tag length K.
Specific embodiment
The present invention includes frame slot processing stage and collision two stages of time slot processing stage, in order to further verify and say
The correctness of the theory analysis of bright the method for the present invention carries out numerical example analysis, and operating process is referring to Fig. 3.
Assuming that it is 8 that system, which has 10 label A~J, the ID of label, ID code is respectively TagA:110101111, TagB:
10100111、TagC:01011111、TagD:10111010、TagE:01111101、TagF:10001011、TagH:
10101110, TagI:01110000, TagJ:11100110, then the specific implementation process is as follows:
(1) in first round search, number of tags N to be identified1=10, m1=3, L1=2m1=8, reader sends inquiry life
Enable Query (m1,L1), labels to be identified all at this time can all respond, responsive tags according to distribution time slot rule Response to selection when
Gap number successfully identifies label A, C, F, G and J, other labels are collision labels.
(2) the highest collision bit k of reader detection collision labels, it can be deduced that k=7, i.e. highest collision bit are the 7th,
And the size of the value q on k is write down, q can be obtainedE=qI=0, qB=qD=qH=1, the second wheel search is carried out, label E and I response exist
The left subtree of binary tree, the right subtree of label B, D and H response in binary tree.
(3) in third round search, label to be identified only has label E and I, and reader detects highest collision bit k, can obtain k=
3, and the value on highest collision bit is respectively qE=1, qI=0, then in right subtree, label I is responded in left subtree for label E response,
Success identifies label E and I.
(4) in fourth round search, number of tags N to be identified2=3, m2=1, L2=2m2=2, reader send inquiry life
Enable Query (m2,L2), label B, D and H response, reader detect highest collision bit k, can obtain k=4, label is according to distribution at this time
Time slot rule selects timeslot number, successfully identifies label D.
In (5) the 5th wheel search, reader detects the highest collision bit of label B and H and records its highest collision bit
On numerical value, q can be obtainedB=0, qH=1, then label B response is in left subtree, label H response in right subtree, successfully identify label B and
H。
(6) all labels all identify that successfully, search terminates.
10 labels to be identified in the method for the present invention identifying system, it is only necessary to 5 search identification is carried out, when the collision of generation
Gap has 5, and free timeslot has 1, other are successfully time slot, and the total timeslot number needed is 16, relative to DFSA algorithm and
RBST algorithm reduces required total timeslot number in searching times and identification process.
Performance evaluation of the invention:
Present invention incorporates a kind of novel anticollisions that dynamic Frame Slotted Aloha algorithm and Binary tree search algorithm propose
Algorithm, the algorithm are reducing total timeslot number, are improving recognition efficiency and contracting compared with DFSA algorithm, RBST algorithm and AHT algorithm
There is apparent advantage in terms of short recognition time, and algorithm design is simpler, the recognition efficiency of algorithm is not by tag ID length
Influence, increase the stability of system identification efficiency, inside tags do not need the complicated circuit of design, and it is hard can to reduce label
The cost of part, therefore the research approach has a good application prospect in terms of current technology of Internet of things.
Experimental result and experimental data of the present invention Intel Core, CPU [email protected], 2GB memory and
It in the environment of Windows7, is emulated using MATLAB software platform, has carried out 7 groups of contrast simulation experiments respectively.
(1) total timeslot number notional result and analysis of simulation result
Total timeslot number required for reader identification label process is expressed as required for dynamic Frame Slotted Aloha algorithm always
The sum of total timeslot number required for timeslot number and Binary tree search algorithm.The average total timeslot number theoretical value finally obtained isThe comparison for indicating total timeslot number notional result and simulation result as shown in Figure 4 show that the two is basic and keeps one
It causes, error is less than 2%.
(2) recognition efficiency notional result and analysis of simulation result
When the recognition efficiency of system indicates to be served only for the readable time slot of identification label and identify total required for these labels
The ratio of gap number, analysis show that the theoretical value of average recognition efficiency isRecognition efficiency is indicated as shown in Figure 5
Notional result and simulation result compare, the error amount both obtained is less than 2%, within the scope of error allows, the two base
Originally it is consistent, further demonstrates the correctness, property of superior reliability of the method for the present invention.
(3) total timeslot number analysis
Total timeslot number is an important indicator of decision systems performance, and total timeslot number is fewer, and system performance is better.It is relevant
Total timeslot number analysis uses following steps:
1. when total number of labels to be identified in RFID system is N, according to formula (16), (17) it can be concluded that determining label selection
The value of timeslot number digit m and the value of frame length L, at this time the value of frame length L be the wheel identify needed for timeslot number, the wheel identification in
Identify that label can select corresponding timeslot number i according to time slot allocation rule, when can generate successfully time slot, collision time slot and free time
Gap；
2. again collision labels of the response in collision time slot are carried out with the detection of highest collision bit, by highest collision bit q=0
Collision labels response in binary left subtree, by the collision labels response of highest collision bit q=1 binary tree right son
Tree, timeslot number needed for which divides collision labels are 2；
3. circuiting sequentially, until all labels to be identified are identified, therefore DFBT algorithm identifies all marks to be identified
Total timeslot number needed for label is the sum of timeslot number needed for timeslot number needed for frame slot processing stage and division collision labels.
To the total of FSA algorithm, DFSA algorithm, RBST algorithm, DFBT algorithm and self-adjusting compound tree anti-collision algorithm (AHT)
Timeslot number is emulated, and simulation result is as shown in Figure 6.Number of tags changes from 0 to 1000, and DFBT algorithm consumes total timeslot number and increases
Add comparison slow, and FSA algorithm and the increased comparison of DFSA algorithm are rapid, especially when number of tags is bigger, DFBT
Algorithm and RBST algorithm consume total timeslot number and linearly increase, and FSA algorithm and DFSA algorithm be it is in exponential increase, when
When total number of labels is close to 1000, DFBT algorithm only needs about 1554 time slots, reduces about 300 than AHT algorithm, subtracts than FSA algorithm
Few about 4011, reduce about 3573 than DFSA algorithm, reduces about 265 than RBST algorithm.
(4) recognition efficiency is analyzed
System identification efficiency is also to measure an important indicator of system performance.When being defined as successfully due to recognition efficiency S
Gap number (total number of labels) N and the ratio between total timeslot number needed for all labels to be identified of identification, therefore the recognition efficiency of DFBT algorithm is only
It is related with total number of labels and total timeslot number.
It is improved on recognition efficiency for verifying DFBT algorithm, comparison algorithm is FSA algorithm, DFSA algorithm, RBST calculation
Method and AHT algorithm, simulation result are as shown in Figure 7.The recognition efficiency of DFBT algorithm may remain in 0.61 or more, and maximum can reach
To 0.64, when total number of labels is identical, the recognition efficiency of DFBT algorithm will be high than three kinds of algorithms of comparison, when label is total
When number reaches 1000, the recognition efficiency ratio FSA algorithm of DFBT algorithm improves about 215%, improves about than DFSA algorithm
210%, about 30% is improved than RBST algorithm.Especially when number of tags is larger, the recognition efficiency of DFBT algorithm still can be with
It is maintained at 0.6 or more, and the recognition efficiency of FSA algorithm and DFSA algorithm sharply declines.
(5) collision timeslot number analysis
From DFBT algorithm flow chart as can be seen that in frame slot processing stage, label response to be identified is when corresponding
In gap i, when two and more than two labels respond on the same timeslot simultaneously, then the time slot is just collision time slot, works as institute
When having the collision labels number in collision time slot to be greater than 2,2 can be generated again in collision labels processing stage division collision labels and is touched
Time slot is hit, therefore DFBT algorithm collision timeslot number caused by identification label process is the collision generated frame slot processing stage
The sum of the collision timeslot number that timeslot number and collision labels processing stage division collision labels generate.
The ratio between total timeslot number needed for being expressed as total number of labels as the recognition efficiency of algorithm and identify these labels, and touch
It hits number of timeslots and directly determines total number of timeslots, so the collision timeslot number generated in identification process influences the identification of algorithm indirectly
Efficiency.Comparing algorithm is RBST algorithm and DFSA algorithm, and simulation result is as shown in figure 8, when total number of labels is identical, DFBT algorithm
The collision timeslot number of generation is minimum, especially when total number of labels increases to 1000 or so, the collision time slot ratio of DFBT algorithm generation
FSA algorithm reduces about 3623, reduces about 3370 than DFSA algorithm, reduces about 396 than RBST algorithm.
(6) recognition time is analyzed
For advantage of the verifying DFBT algorithm on recognition time, comparison algorithm is RBST algorithm and DFSA algorithm, emulation knot
Fruit is as shown in figure 9, total number of labels increases to 1000 from 0, and the tag recognition time also accordingly increases, but the identification of DFBT algorithm
Time is increased compared with RBST algorithm and DFSA algorithm relatively slowly.In the identical situation of total number of labels, when the identification of DFBT algorithm
Between will be short than RBST algorithm and DFSA algorithm, when total number of labels reaches 1000, the recognition time of DFBT algorithm is 1.3 seconds left sides
The right side, and the recognition time of RBST algorithm and DFSA algorithm is respectively 5 seconds and 3 seconds or so, DFBT algorithm greatly shortens label
Recognition time shows that the recognition speed of the algorithm is fast compared with RBST algorithm and DFSA algorithm.
(7) the recognition efficiency analysis of different tag ID length K
For the stability for verifying DFBT algorithm recognition efficiency, 16bit, 32bit and 64bit are taken to tag ID length K respectively
Experimental comparison is carried out, when one timing of total number of labels, recognition efficiency is mainly influenced by total timeslot number, and total according to formula (17)
Timeslot number is related with label selection timeslot number digit m is determined, i.e., the digit with the binary number after tag ID highest collision bit has
It closes, with tag ID length without direct relation.
Simulation result is as shown in Figure 10, under different tag ID length, the recognition efficiency kept stable of DFBT algorithm.
The stability of system identification efficiency can be increased to demonstrate new algorithm proposed by the present invention.
Claims (1)
1. a kind of binary tree RFID anti-collision method based on dynamic frame slot, it is characterized in that being realized by following steps:
(S01): before carrying out reading data, estimating the number N of label to be identified, N ∈ [1,1000] with Vogt algorithm first；
(S02): calculating separately out the value of the value and frame length L that determine label selection timeslot number digit m, in which:
Indicate downward rounding operation, N ∈ [1,1000], then [1,9] m ∈, L ∈ [2,512]；
(S03): time slot scanning is carried out, reader sends Query (m, L) and orders to each label, after label receives order, according to
Timeslot number i, i ∈ [0,511] needed for distribution time slot rule selects respectively in (0, L-1) time slot；
(S04): reader judges the state of i-th of time slot；Then execute following three kinds of situations one of:
If 1) time slot is that successfully time slot, the number N of label to be identified subtract 1, go to step after the wheel end of identification
(S09)；
If 2) time slot is empty slot, go to step (S01), carries out next round identification；
If 3) time slot is collision time slot, all collision labels go to step (S05)；
(S05): the highest collision bit k value of the collision labels ID code of reader detection on last stage, the value of k depend on ID code
Length, if tag ID code is 8, then [0,7] k ∈；And the size of the numerical value q on highest collision bit k is detected and records, due to
The form of tag memory storage is binary system, so q=0 or q=1；
(S06): tag ID highest collision bit and prefix are sent to each collision labels by reader, right with collision labels ID itself
Than counting if detecting the label highest collision bit of transmission and prefix is identical as collision labels ID and highest collision bit is " 0 "
Device LSC is counted from adding 1, if collision bit is " 1 ", counter RSC adds 1 certainly；
(S07): reader is entered a judgement: right subtree of the label response of all q=1 in binary tree, the label of all q=0
The left subtree in binary tree is responded, the value of counter LSC and counter RSC is obtained, i.e., responds respectively on a left side for binary tree
Number of tags in subtree and right subtree；
(S08): the value of counter LSC and counter RSC are judged:
If LSC > 2 and RSC > 2, step (S02) is jumped to；
If LSC=2 and RSC=2, step (S05) is jumped to；
If LSC=1 and RSC=1, show this wheel end of identification, then the number N of label to be identified subtracts 2；
(S09): whether label is all identified after judging last round of end of identification: if number N > 1 of label to be identified, illustrating to mark
Label not all identification, then jump to step (S01), carry out the identification of next round, until whole tag recognitions are completed；If to be identified
When the number N of label=0, care label all complete by identification, then searches for end of identification.
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| CN106599958A (en) * | 2016-12-23 | 2017-04-26 | 西京学院 | RFID-based agricultural product tracing batch tag information acquisition method |
| CN107506676B (en) * | 2017-08-22 | 2020-11-03 | 哈尔滨工程大学 | RFID anti-collision method based on dynamic detection point and label estimation |
| CN108280377B (en) * | 2018-02-10 | 2021-01-26 | 沈阳理工大学 | Anti-collision reading method for multiple labels in material warehousing management system |
| CN108959993A (en) * | 2018-05-17 | 2018-12-07 | 吉林大学 | A kind of RFID tag mixing anti-collision algorithm suitable for the cultivation of intelligent flock of sheep |
| CN111178100B (en) * | 2019-12-26 | 2024-02-13 | 大连大学 | Radio Frequency Identification (RFID) tag number estimation method |
| CN111818475A (en) * | 2020-07-02 | 2020-10-23 | 四川长虹电器股份有限公司 | Multispectral instrument distributed detection system based on internet of things technology |
| CN112446448B (en) * | 2020-11-13 | 2022-10-04 | 昆明理工大学 | Method for identifying RFID (radio frequency identification) label by single reader combining DFSA (distributed feedback system) and binary algorithm |
| CN112949336B (en) * | 2021-03-06 | 2024-04-16 | 南京航空航天大学 | Label anti-collision algorithm based on parallel tree splitting |
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